The utility of computer vision systems in a variety of applications is widely recognized. A fundamental task in computer vision systems is determining the pose of the image capturing device (e.g., a video camera) given one or more images of known points in the world.
An exemplary application of pose estimation is vehicle localization. By tracking environmental features from a vehicle-mounted camera, it is possible to estimate changes in vehicle position and to use this information for navigation, tracking or other purposes. However, current techniques for camera-based vehicle localization that are based on known pose estimation algorithms do not make optimal use of multiple cameras present on the vehicle, because existing methods for pose estimation assume a single-perspective camera model. As a result of this limitation, ad hoc pose estimation methods are used. For example, pose may be estimated independently for each vehicle-mounted camera and the separate pose estimates subsequently combined. Thus, such methods do not generally make the best use of available information.
By way of further example, one known method for determining the pose of a calibrated perspective camera implements the images of three known points in the world in order to constrain the possible poses of the camera to up to four pairs of solutions (where no more than one solution from each pair is valid). These solutions are typically generated in accordance with the known “three-point perspective pose problem”. Though this approach can be successfully applied in many circumstances, its utility, as it stands, is limited to particular camera geometries and viewpoints. Thus, this approach is less applicable to camera models having more generalized geometries (e.g., geometries that do not adhere to a central perspective model or correspond to a single viewpoint), which have become increasingly popular tools in computer vision systems.
Therefore, there is a need in the art for a method and apparatus for determining camera pose that is substantially model-independent.